Detector with focus: Normalizing gradient in image pyramid

An image pyramid can extend many object detection algorithms to solve detection on multiple scales. However, interpolation during the resampling process of an image pyramid causes gradient variation, which is the difference of the gradients between the original image and the scaled images. Our key i...

Full description

Saved in:
Bibliographic Details
Main Authors: Kim, Yonghyun, Kang, Bong-Nam, Kim, Daijin
Format: Conference Proceeding
Language:English
Subjects:
Data models
Deformable models
detection
Detectors
gradient
Interpolation
normalization
Object detection
Pose estimation
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:An image pyramid can extend many object detection algorithms to solve detection on multiple scales. However, interpolation during the resampling process of an image pyramid causes gradient variation, which is the difference of the gradients between the original image and the scaled images. Our key insight is that the increased variance of gradients makes the classifiers have difficulty in correctly assigning categories. We prove the existence of the gradient variation by formulating the ratio of gradient expectations between an original image and scaled images, then propose a simple and novel gradient normalization method to eliminate the effect of this variation. The proposed normalization method reduce the variance in an image pyramid and allow the classifier to focus on a smaller coverage. We show the improvement in three different visual recognition problems: pedestrian detection, pose estimation, and object detection. The method is generally applicable to many vision algorithms based on an image pyramid with gradients.
ISSN:2381-8549
DOI:10.1109/ICIP.2017.8296315